| Characteristic | N = 1471 |
|---|---|
| Age | – |
| Median (Q1, Q3) | 37 (34, 44) |
| Unknown | 13 |
| Age group | – |
| age ≥15-<25 | 1/134 (0.7%) |
| age ≥25-<35 | 45/134 (34%) |
| age ≥35-<45 | 62/134 (46%) |
| age ≥45-<55 | 19/134 (14%) |
| age ≥55 | 7/134 (5.2%) |
| Unknown | 13 |
| Sex | – |
| Female | 38/134 (28%) |
| Male | 96/134 (72%) |
| Unknown | 13 |
| Period | – |
| First Three Days | 74/147 (50%) |
| Last Two Days | 73/147 (50%) |
| Years of Professional Experience | – |
| Median (Q1, Q3) | 11 (8, 17) |
| Unknown | 13 |
| Experience in VHF Response | 101/134 (75%) |
| Unknown | 13 |
| The IDTM is an Advantage to the Systems Used in the Past | 117/121 (97%) |
| Unknown | 26 |
| Envisioning Usage of IDMT in the Future | – |
| Yes | 121/121 (100%) |
| Unknown | 26 |
| 1 n/N (%) | |
| Characteristic | Overall N = 1471 |
First Three Days N = 741 |
Last Two Days N = 731 |
p-value2 |
|---|---|---|---|---|
| Age | – | – | – | 0.67 |
| Median (Q1, Q3) | 37 (34, 44) | 37 (34, 43) | 37 (34, 44) | – |
| Unknown | 13 | 8 | 5 | – |
| Age group | – | – | – | 0.89 |
| age ≥15-<25 | 1/134 (0.7%) | 1/66 (1.5%) | 0/68 (0%) | – |
| age ≥25-<35 | 45/134 (34%) | 22/66 (33%) | 23/68 (34%) | – |
| age ≥35-<45 | 62/134 (46%) | 32/66 (48%) | 30/68 (44%) | – |
| age ≥45-<55 | 19/134 (14%) | 8/66 (12%) | 11/68 (16%) | – |
| age ≥55 | 7/134 (5.2%) | 3/66 (4.5%) | 4/68 (5.9%) | – |
| Unknown | 13 | 8 | 5 | – |
| Sex | – | – | – | 0.78 |
| Female | 38/134 (28%) | 18/66 (27%) | 20/68 (29%) | – |
| Male | 96/134 (72%) | 48/66 (73%) | 48/68 (71%) | – |
| Unknown | 13 | 8 | 5 | – |
| Years of Professional Experience | – | – | – | 0.66 |
| Median (Q1, Q3) | 11 (8, 17) | 11 (8, 17) | 12 (8, 18) | – |
| Unknown | 13 | 8 | 5 | – |
| Experience in VHF Response | 101/134 (75%) | 50/66 (76%) | 51/68 (75%) | 0.92 |
| Unknown | 13 | 8 | 5 | – |
| The IDTM is an Advantage to the Systems Used in the Past | 117/121 (97%) | 58/61 (95%) | 59/60 (98%) | 0.62 |
| Unknown | 26 | 13 | 13 | – |
| Envisioning Usage of IDMT in the Future | – | – | – | – |
| Yes | 121/121 (100%) | 61/61 (100%) | 60/60 (100%) | – |
| Unknown | 26 | 13 | 13 | – |
| 1 n/N (%) | ||||
| 2 Wilcoxon rank sum test; Fisher’s exact test; Pearson’s Chi-squared test | ||||
Each item was rated on a 7-point scale from the left concept to the right. Lower values indicate stronger agreement with the first term (e.g., ‘Annoying’), while higher values reflect stronger agreement with the second (e.g., ‘Enjoyable’).
We used an ordinal logistic regression model to account for the ordered nature of the response scale. This allowed us to estimate the likelihood of each response level across time periods or experience in VHF response and visualize these shifts using the predicted probability plot.
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.